Rapid Prediction of Blast Loading in Dense Urban Building Complex Based on Neural Networks

doi:10.12382/bgxb.2024.0987

Abstract

Abstract:

The staggered layout of urban building complex makes the propagation path of explosion shock wave more complicated,thereby increasing the difficulty of evaluating the damage effect comprehensively and accurately.Numerical simulation methods based on computational fluid dynamics can accurately simulate the blast loading,but the calculated amount is large and the calculation time is long.In order to rapidly predict the blast loading in urban building complex,a fast blast loading prediction method based on neural network is proposed.The influence of the number of training samples on the prediction accuracy and the effect of regional division on the prediction performance of the model are analyzed.In order to meet the data requirements for training the neural network model,the explosion simulation software is used to analyze the mesh sensitivity in a typical dense urban building complex and generate a dataset for 80 sets of explosion scenarios while considering simulation speed and accuracy.In order to determine the appropriate model structure,the fully connected neural networks with different numbers of layers are constructed for comparative experiment and analysis.The effects of the number of training samples,the division of region and the construction of dual models on the prediction accuracy of the model are analyzed through comparative experiments.The results show that the prediction error of the proposed method is less than 10% on 16 sets of test data except the training data,and the inference time only takes 2 seconds.The proposed method has a balanced and good prediction ability for various ranges of peak overpressure,and provides a new approach and perspective for realizing the rapid prediction of blast loading in urban building complex.

Key words: complex urban environment, blast loading, neural network, rapid prediction

HUANG Peiji, PENG Weiwen, LENG Chunjiang, ZHANG Qing, ZHONG Wei. Rapid Prediction of Blast Loading in Dense Urban Building Complex Based on Neural Networks[J]. Acta Armamentarii, 2025, 46(8): 240987-.

Figures/Tables 22

References 27

[1]	RIGBY S E, LODGE T J, ALOTAIBI S, et al. Preliminary yield estimation of the 2020 Beirut explosion using video footage from social media[J]. Shock Waves, 2020, 30(6):671-675.
[2]	曹涛, 孙浩, 周游, 等. 近地爆炸冲击波传播特性数值模拟与应用[J]. 兵器装备工程学报, 2020, 41(12):187-191.
	CAO T, SUN H, ZHOU Y, et al. Numerical simulation and application of propagation characteristics of shock wave near ground explosion[J]. Journal of Ordnance Equipment Engineering, 2020, 41(12):187-191. (in Chinese)
[3]	张云峰, 陈博, 魏欣, 等. 空气自由场爆炸冲击波数值建模及应用[J]. 爆炸与冲击, 2023, 43(11):114202.
	ZHANG Y F, CHEN B, WEI X, et al. Numerical modeling and application of shock wave of free-field air explosion[J]. Explosion and Shock Waves, 2023, 43(11):114202. (in Chinese)
[4]	张晓颖, 李胜杰, 李志强. 爆炸载荷作用下夹层玻璃动态响应的数值模拟[J]. 兵工学报, 2018, 39(7):1379-1388. doi: 10.3969/j.issn.1000-1093.2018.07.016
	ZHANG X Y, LI S J, LI Z Q. Numerical simulation of dynamic response of laminated glass subjected to blast load[J]. Acta Armamentarii, 2018, 39(7):1379-1388. (in Chinese)
[5]	赵海涛, 王成. 空中爆炸问题的高精度数值模拟研究[J]. 兵工学报, 2013, 34(12):1536-1546. doi: 10.3969/j.issn.1000-1093.2013.12.008
	ZHAO H T, WANG C. High resolution numerical simulation of air explosion[J]. Acta Armamentarii, 2013, 34(12):1536-1546. (in Chinese)
[6]	SHI Y C, LIU S Z, LI Z X, et al. Review on quick safety assessment of building structures in complex urban environment after extreme explosion events[J]. International Journal of Protective Structures, 2023, 14(3):438-458.
[7]	REMENNIKOV A M, MENDIS P A. Prediction of airblast loads in complex environments using artificial neural networks[J]. WIT Transactions on The Built Environment, 2006, 87:269-278.
[8]	FLOOD I, BEWICK B T, RAUCH E. Rapid simulation of blast wave propagation in built environments using coarse-grain simulation[J]. International Journal of Protective Structures, 2012, 3(4):431-448.
[9]	PANNELL J J, RIGBY S E, PANOUTSOS G. Physics-informed regularisation procedure in neural networks:an application in blast protection engineering[J]. International Journal of Protective Structures, 2022, 13(3):555-578.
[10]	PANNELL J J, RIGBY S E, PANOUTSOS G. Application of transfer learning for the prediction of blast impulse[J]. International Journal of Protective Structures, 2023, 14(2):242-262.
[11]	DENNIS A A, RIGBY S E. The direction-encoded neural network:a machine learning approach to rapidly predict blast loading in obstructed environments[J]. International Journal of Protective Structures, 2024, 15(3):455-483.
[12]	LI Q L, WANG Y, LI L, et al. Prediction of BLEVE loads on structures using machine learning and CFD[J]. Process Safety and Environmental Protection, 2023, 171:914-925.
[13]	HUANG Y, ZHU S J, CHEN S W. Deep learning-driven super-resolution reconstruction of two-dimensional explosion pressure fields[J]. Journal of Building Engineering, 2023, 78:107620.
[14]	杨森. 城市建筑群爆炸荷载预测及灾害效应快速评估[D]. 天津: 天津大学, 2021.
	YANG S. Prediction of blast loads and rapid evaluation of disaster effects in complex urban environments[D]. Tianjin: Tianjin University, 2021. (in Chinese)
[15]	陈梓薇, 王仲琦, 曾令辉. 基于BP神经网络的爆炸用激波管峰值压力预测方法[J]. 爆炸与冲击, 2024, 44(5):054101.
	CHEN Z W, WANG Z Q, ZENG L H. A method for predicting peak pressure in an explosion shock tube based on BP neural network[J]. Explosion and Shock Waves, 2024, 44(5):054101. (in Chinese)
[16]	LI Q L, LI L, SHAO Y D, et al. A multi-task machine learning approach for data efficient prediction of blast loading[J]. Engineering Structures, 2025, 326:119577.
[17]	SHI J, LI J, TAM W C, et al. Physics_GNN:Towards physics-informed graph neural network for the real-time simulation of obstructed gas explosion[J]. Reliability Engineering & System Safety, 2025, 256:110777.
[18]	徐永康, 薛琨. 基于人工神经网络算法的多相云雾爆轰毁伤效应预测模型[J]. 兵工学报, 2024, 45(6):1889-1905. doi: 10.12382/bgxb.2023.0094
	XU Y K, XUE K. Artificial neural network-based prediction model for damage effect of fuel-air explosive[J]. Acta Armamentarii, 2024, 45(6):1889-1905. (in Chinese) doi: 10.12382/bgxb.2023.0094
[19]	SHENG K K, LI Y, XU X, et al. Stator and rotor temperature prediction of permanent magnet synchronous motor based on BP neural network[C]// Proceedings of the 2024 43rd Chinese Control Conference. Kunming,China: IEEE, 2024:8832-8837.
[20]	ZUO K J, YE Z Y, BU S H, et al. Fast simulation of airfoil flow field via deep neural network[J]. Aerospace Science and Technology, 2024, 150:109207.
[21]	BRITTLE M A. Blast propagation in a geometrically complex environment[D]. Cranfield,UK: Cranfield University, 2004.
[22]	VALGER S A, FEDOROVA N N, FEDOROVA A V. Numerical simulation of blast action on civil structures in urban environment[J]. IOP Conference Series:Materials Science and Engineering. IOP Publishing, 2017, 245(6):062018.
[23]	HEYLMUN J, VONK P, BREWER T. Blastfoam theory and user guide[Z/OL]. Brazos Street Austin,TX,US: Synthetik Applied Technologies, 2019(2019-10-30). www.blastfoam.org/blastFoam_User_Guide.pdf.
[24]	JASAK H. OpenFOAM:open source CFD in research and industry[J]. International Journal of Naval Architecture and Ocean Engineering, 2009, 1(2):89-94.
[25]	LI J, HAO H. Numerical and analytical prediction of pressure and impulse from vented gas explosion in large cylindrical tanks[J]. Process Safety and Environmental Protection, 2019, 127:226-244.
[26]	REMENNIKOV A, GAN E C J, TAN S S, et al. Methodology for predicting explosion risk around underground coal mine openings towards developing exclusion zones[C]// Proceedings of the 2022 Resource Operators Conference. Wollongong,Australia: University of Wollongong Press, 2022.
[27]	DENNIS A A, PANNELL J J, SMYL D J, et al. Prediction of blast loading in an internal environment using artificial neural networks[J]. International Journal of Protective Structures, 2021, 12(3):287-314.

测点编号	x	y	z
T1	0.30	1.1	0.105
T11	-0.30	1.0	0.075
T12	1.26	1.0	0.075

测点编号	x	y	z
T1	0.30	1.1	0.105
T11	-0.30	1.0	0.075
T12	1.26	1.0	0.075

A/kPa	B/kPa	R₁	R₂
3.737×10⁸	3.737×10⁶	4.15	0.9
λ	E/(kJ·m^-3)	ρ₀/(kg·m^-3)
0.35	6.0×10⁶	1 630

A/kPa	B/kPa	R₁	R₂
3.737×10⁸	3.737×10⁶	4.15	0.9
λ	E/(kJ·m^-3)	ρ₀/(kg·m^-3)
0.35	6.0×10⁶	1 630

参数	数值
TNT爆源尺寸/g	4,8,12,16,20,24,28,32
爆源位置/m	爆源1(0.478,0.35),爆源2(0.678,0.35),爆源3(0.878,0.35),爆源4(0.478,0.75),爆源5 (0.278,0.75),爆源6 (0.078,0.75),爆源7(0.180,0.10),爆源8(0.800,1.10),爆源9(-0.150,1.10),爆源10(-0.150,0.30)